Datasets:
HenryAI
/
KerasCodeExamples.txt

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x = layers.SeparableConv2D(
NUM_KEYPOINTS, kernel_size=5, strides=1, activation=\"relu\"
)(x)
outputs = layers.SeparableConv2D(
NUM_KEYPOINTS, kernel_size=3, strides=1, activation=\"sigmoid\"
)(x)
return keras.Model(inputs, outputs, name=\"keypoint_detector\")
Our custom network is fully-convolutional which makes it more parameter-friendly than the same version of the network having fully-connected dense layers.
get_model().summary()
Model: \"keypoint_detector\"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_2 (InputLayer) [(None, 224, 224, 3)] 0
_________________________________________________________________
tf.math.truediv (TFOpLambda) (None, 224, 224, 3) 0
_________________________________________________________________
tf.math.subtract (TFOpLambda (None, 224, 224, 3) 0
_________________________________________________________________
mobilenetv2_1.00_224 (Functi (None, 7, 7, 1280) 2257984
_________________________________________________________________
dropout (Dropout) (None, 7, 7, 1280) 0
_________________________________________________________________
separable_conv2d (SeparableC (None, 3, 3, 48) 93488
_________________________________________________________________
separable_conv2d_1 (Separabl (None, 1, 1, 48) 2784
=================================================================
Total params: 2,354,256
Trainable params: 96,272
Non-trainable params: 2,257,984
_________________________________________________________________
Notice the output shape of the network: (None, 1, 1, 48). This is why we have reshaped the coordinates as: batch_keypoints[i, :] = np.array(kp_temp).reshape(1, 1, 24 * 2).
Model compilation and training
For this example, we will train the network only for five epochs.
model = get_model()
model.compile(loss=\"mse\", optimizer=keras.optimizers.Adam(1e-4))
model.fit(train_dataset, validation_data=validation_dataset, epochs=EPOCHS)
Epoch 1/5
166/166 [==============================] - 85s 486ms/step - loss: 0.1087 - val_loss: 0.0950
Epoch 2/5
166/166 [==============================] - 78s 471ms/step - loss: 0.0830 - val_loss: 0.0778
Epoch 3/5
166/166 [==============================] - 78s 468ms/step - loss: 0.0778 - val_loss: 0.0739
Epoch 4/5
166/166 [==============================] - 78s 470ms/step - loss: 0.0753 - val_loss: 0.0711
Epoch 5/5
166/166 [==============================] - 78s 468ms/step - loss: 0.0735 - val_loss: 0.0692
<tensorflow.python.keras.callbacks.History at 0x7f3ac55b6050>
Make predictions and visualize them
sample_val_images, sample_val_keypoints = next(iter(validation_dataset))
sample_val_images = sample_val_images[:4]
sample_val_keypoints = sample_val_keypoints[:4].reshape(-1, 24, 2) * IMG_SIZE
predictions = model.predict(sample_val_images).reshape(-1, 24, 2) * IMG_SIZE
# Ground-truth
visualize_keypoints(sample_val_images, sample_val_keypoints)
# Predictions
visualize_keypoints(sample_val_images, predictions)
png
png
Predictions will likely improve with more training.
Going further
Try using other augmentation transforms from imgaug to investigate how that changes the results.
Here, we transferred the features from the pre-trained network linearly that is we did not fine-tune it. You are encouraged to fine-tune it on this task and see if that improves the performance. You can also try different architectures and see how they affect the final performance.
Implementation of classical Knowledge Distillation.
Introduction to Knowledge Distillation
Knowledge Distillation is a procedure for model compression, in which a small (student) model is trained to match a large pre-trained (teacher) model. Knowledge is transferred from the teacher model to the student by minimizing a loss function, aimed at matching softened teacher logits as well as ground-truth labels.
The logits are softened by applying a \"temperature\" scaling function in the softmax, effectively smoothing out the probability distribution and revealing inter-class relationships learned by the teacher.
Reference:
Hinton et al. (2015)
Setup
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import numpy as np
Construct Distiller() class
The custom Distiller() class, overrides the Model methods train_step, test_step, and compile(). In order to use the distiller, we need:
A trained teacher model
A student model to train
A student loss function on the difference between student predictions and ground-truth
A distillation loss function, along with a temperature, on the difference between the soft student predictions and the soft teacher labels
An alpha factor to weight the student and distillation loss
An optimizer for the student and (optional) metrics to evaluate performance
In the train_step method, we perform a forward pass of both the teacher and student, calculate the loss with weighting of the student_loss and distillation_loss by alpha and 1 - alpha, respectively, and perform the backward pass. Note: only the student weights are updated, and therefore we only calculate the gradients ...